Spherical Interpolation for Backward-Compatible Multimodal Representations

arXiv:2609.39836 · cs.CV, cs.LG · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/miccunifi/SLERP_backward_compatibility

Terminology

• Alignment — 110× in this paper · explained in 1 episode
• Embeddings — 43× in this paper · explained in 2 episodes
• Benchmark — 16× in this paper · explained in 2 episodes
• Cosine Similarity — 9× in this paper · explained in 3 episodes
• Representation Learning — 8× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Core — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Regularization — 5× in this paper · explained in 3 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• International Conference on Learning Representations — 3× in this paper
• Latent Space — 3× in this paper · explained in 7 episodes
• Modality Gap — 3× in this paper · explained in 4 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Institutional Review Board — 2× in this paper
• Model Merging — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 14 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• HuggingFace's Transformers: State-of-the-art Natural Language Processing
• Billion-scale similarity search with GPUs
• LLaMA: Open and Efficient Foundation Language Models
• Textbooks Are All You Need
• Canonicalizing Multimodal Contrastive Representation Learning
• Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
• WARP: On the Benefits of Weight Averaged Rewarded Policies
• Multi-Way Representation Alignment
• Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging

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